Connecting Financial Catalysts in a Neo4j Graph

Traditionally, market price feeds and incoming corporate catalyst feeds live in separate worlds. SQL schemas struggle to represent complex network behaviors, such as how one executive joining a board ripples across competitor valuations or supply chains.

In this post, we explore how graph modeling natively links time-series price data and qualitative market events under a unified ontology.

The Model Architecture

At the heart of the system, we represent corporate metadata and events as nodes and edges. Rather than running index scans across date columns, we define a physical chain of price points:

(Company) -[:HAS_PRICE_POINT]-> (PricePoint) -[:NEXT]-> (PricePoint) -[:NEXT]-> (PricePoint)

By linking each PricePoint chronologically to the :NEXT price point, we can query historical trends and splits with simple traversal paths.

Introducing Catalysts

When news hits (for example, IFTTT webhook feeds orparsed Swedish market close emails), we insert new Event nodes and link them:

MATCH (c:Company {ticker: "HM.B"})
CREATE (e:Event {
  title: "Goldman Sachs upgrades H&M to BUY", 
  impact: "positive", 
  date: "2026-06-28"
})
CREATE (c)-[:HAS_EVENT]->(e)

Traversing relationships

With the graph fully populated, executing traversals to run sector checks or map catalyst-to-price response is highly efficient:

MATCH (c:Company)-[:HAS_EVENT]->(e:Event)
WHERE e.impact = "positive"
MATCH (c)-[:HAS_PRICE_POINT]->(p:PricePoint)
WHERE p.date = e.date
RETURN c.ticker, e.title, p.close_price

This returns instant reports on stock prices precisely on the day positive catalysts occur, showing the immediate power of connected financial data.